Model-based Response surface optimization with Bootstrap Sampling - Surface interpolation with XGBoost, gradient ascent path finding, and clustering-based optimal point estimation
Project description
MRBS
Model-based Response surface optimization with Bootstrap Sampling
部分的なデータをXGBoostで補完し、勾配上昇法とクラスタリングで最適点を探索するPythonライブラリです。
特徴
- XGBoost補完: 部分的なサンプリングデータから曲面を補完
- 勾配上昇法: 近傍線形回帰による局所勾配推定で最適点を探索
- クラスタリング: 複数の探索パスの終点をK-Meansでクラスタリングし、最適点候補を算出
インストール
pip install mrbs
開発版のインストール:
git clone https://github.com/yutotakagi/MRBS.git
cd MRBS
pip install -e ".[dev]"
クイックスタート
import numpy as np
import pandas as pd
from mrbs import SurfaceInterpolator, GradientAscentOptimizer, OptimalPointFinder
from mrbs.visualization import plot_optimization_result
# 1. データ読み込み・補完
df = pd.read_csv("data/sphere_sampled_benchmark_xy_F.csv")
X = df[["x", "y"]].values
y = df["F"].values
interpolator = SurfaceInterpolator(n_estimators=300)
interpolator.fit(X, y)
xx, yy, F_grid = interpolator.generate_grid(n_grid=100)
# 2. 勾配上昇パス計算
optimizer = GradientAscentOptimizer(interpolator, n_steps=10, radius=10)
start_points = np.random.uniform(-100, 100, size=(20, 2))
paths = optimizer.compute_paths(start_points)
# 3. クラスタリング・最適点算出
finder = OptimalPointFinder(k_min=1, k_max=5)
result = finder.find_optimal_points(paths)
# 4. 結果表示
print("最適点候補:")
for i, centroid in enumerate(result.centroids):
print(f" Point {i+1}: x={centroid[0]:.4f}, y={centroid[1]:.4f}")
# 5. 可視化
plot_optimization_result(xx, yy, F_grid, result)
モジュール構成
| モジュール | 説明 |
|---|---|
mrbs.interpolator |
SurfaceInterpolator - XGBoostによる曲面補完 |
mrbs.gradient_ascent |
GradientAscentOptimizer - 勾配上昇法による最適点探索 |
mrbs.clustering |
OptimalPointFinder - クラスタリングによる最適点算出 |
mrbs.visualization |
可視化関数 |
依存パッケージ
- Python >= 3.9
- numpy >= 1.20
- pandas >= 1.3
- xgboost >= 1.5
- scikit-learn >= 1.0
- matplotlib >= 3.4
コントリビューション
コントリビューションを歓迎します！詳細は CONTRIBUTING.md をご覧ください。
このプロジェクトは Contributor Covenant に基づく行動規範を採用しています。
ライセンス
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